Screening for Chilling‐Tolerant Soybeans at the Flowering Stage Using a Seed Yield‐ and Maturity‐Based Evaluation Method
Bibliographic record
Abstract
ABSTRACT Cold weather damages soybean [ Glycine max (L.) Merr.] crops in high‐latitude countries. The decreased seed yields caused by low temperatures are attributed to three main factors: poor growth during the early growth stage, abscission of flowers and pods at the flowering stage, and insufficient grain filling at the pod‐filling stage. The abscission of flowers and pods is the most important factor that contributes to reduced yields. There are differences in chilling tolerance among cultivars developed in Japan at the flowering stage. This study screened for chilling‐tolerant (CT) soybeans developed in high‐latitude countries, such as Canada, Switzerland, Poland, and the Czech Republic. For the screening, plants were subjected to a 28‐d chilling‐temperature treatment after flowering in a phytotron, and six CT cultivars, ‘Maple Arrow’, ‘AC Proteus’, ‘Ceresia, Pelvoux’, ‘Silvia’, and ‘Mazowia’, were found by focusing on seed yield and maturity. These six CT cultivars matured earlier and had greater yields than Japanese cultivars in the field under severe chilling conditions. Moreover, we developed five breeding lines derived from a cross of the CT cultivars Mazowia and ‘Toyoharuka’ (TH). All five breeding lines matured earlier than TH and had yields similar to that of TH in the field under normal conditions. Phytotron tests revealed that the chilling tolerance levels of two of the breeding lines were slightly greater than that of TH. The six CT cultivars found in this study will be useful for chilling tolerance breeding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".